feat: 生成回忆录agent结构封装

api/app/agents/memoir/extraction_agent.py

@@ -0,0 +1,66 @@
+"""
+ExtractionAgent：从用户消息中提取 5-stage 状态与 slots。
+对应现有逻辑：get_state_extraction_prompt + JSON 解析
+"""
+from __future__ import annotations
+
+import json
+from dataclasses import dataclass
+from typing import Any, Dict
+
+from app.core.logging import get_logger
+from app.features.memoir.memoir_images.json_payload import extract_json_payload
+
+from app.agents.prompts.memory_prompts import get_state_extraction_prompt
+
+logger = get_logger(__name__)
+
+
+@dataclass
+class ExtractionResult:
+    """状态提取结果"""
+    detected_stage: str
+    slots: Dict[str, str]
+
+
+class ExtractionAgent:
+    """从用户消息中提取 detected_stage 和 slots"""
+
+    def extract(
+        self,
+        user_message: str,
+        current_stage: str,
+        stage_slots: Dict[str, Any],
+        llm: Any,
+    ) -> ExtractionResult:
+        """
+        提取结构化信息并判断阶段。
+        llm 需支持 .invoke(prompt) 同步调用（Celery 任务内使用）。
+        """
+        detected_stage = current_stage
+        extracted_slots: Dict[str, str] = {}
+
+        if not llm:
+            return ExtractionResult(detected_stage=detected_stage, slots=extracted_slots)
+
+        try:
+            prompt = get_state_extraction_prompt(
+                user_message=user_message,
+                current_stage=current_stage,
+                stage_slots={
+                    k: v.model_dump() if hasattr(v, "model_dump") else v
+                    for k, v in (stage_slots or {}).items()
+                },
+            )
+            response = llm.invoke(prompt)
+            parsed = json.loads(extract_json_payload(response.content))
+            detected_stage = parsed.get("detected_stage", detected_stage)
+            raw_slots = parsed.get("slots", {}) or {}
+            extracted_slots = {
+                k: v if isinstance(v, str) else str(v)
+                for k, v in raw_slots.items()
+            }
+        except (json.JSONDecodeError, Exception) as e:
+            logger.warning("ExtractionAgent LLM 解析失败: %s", e)
+
+        return ExtractionResult(detected_stage=detected_stage, slots=extracted_slots)